摘要近年来, 联邦学习作为解决数据孤岛问题的技术被广泛关注, 已经开始被应用于金融,
医疗健康以及智慧城市等领域. 从3 个层面系统阐述联邦学习算法. 首先通过联邦学习的定义 …

群体智能是在互联网高速普及下诞生的人工智能新范式. 然而, 数据孤岛与数据隐私保护问题
导致群体间数据共享困难, 群体智能应用难以构建. 联邦学习是一类新兴的打破数据孤岛 …

Federated learning is a new learning paradigm for extracting knowledge from distributed
data. Due to its favorable properties in preserving privacy and saving communication costs …

Federated learning is a new machine learning paradigm. The goal is to build a machine
learning model from the data sets distributed on multiple devices–so-called an isolated data …

Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …

Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …

In recent years, federated learning has received widespread attention as a technology to
solve the problem of data islands, and it has begun to be applied in fields such as finance …

在大数据背景下, 保证数据可信共享是数据联邦的基本要求. 区块链技术代替传统的主从架构,
可以提高联邦学习 (federated learning, FL) 的安全性. 然而, 现有工作中, 模型参数验证与数据 …

With the emergence of AI technologies, intrinsic value of data is released and takes
tremendous effects on numerous industries. In the context of regional medical care, data …

We are delighted to present this special issue on Federated Learning: Algorithms, Systems,
and Applications. Federated Learning enables to collaboratively learn a shared learning …